Introduction

Physical risks

Transition Risks

Economics of Climate Change and Green
Finance
Antoine Mandel1
Cours Bachelier
February 14, 2020

1

antoine.mandel@univ-paris1.fr

Introduction

Physical risks

Transition Risks

Summary

A short introduction to current issues in the economics ofclimate change through the lens of financial stability.
Economic and financial impacts of climate change(“physical risks")
Economic and financial impacts of climate change
mitigation (“transition risks")

Introduction

Physical risks

Outline1

Introduction

2

Physical risks

3

Transition Risks

Transition Risks

Introduction

Physical risks

Transition Risks

Motivation: Climate change and financial stability
Concerns among financial supervisors of impacts of climatechange on financial stability:
BoE Governor McCarney’ s speech on “breaking the tragedy ofthe horizon" (2015) emphasizes that
Shifts in our climate bring potentially profound implications for
insurers, financial stability and the economy.[...] once climatechange becomes a defining issue for financial stability, it may
already be too late.
Central Banks and Supervisors Network for Greening the
Financial System (NGFS, 2018, 2019) emphasizes that:
Climate-related risks are a source of financial risk. It is therefore
within the mandates of central banks and supervisors to ensure
the financial system is resilient to these risks. [...] The NGFS
recognises that there is a strong risk that climate related
financial risks are not fully reflected in asset valuation.

Introduction

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Transition Risks

Motivation: Lessons from the financial crisis
Concern partly hinges on the precedent of the 2007-2008
financial crisis.
Massive amplification of losses through financial
interdependencies:
Direct losses of the financial system on the US subprime
mortgage market amount to 500 billion USD (Greenlaw etal. 2008)
Triggered losses one order of magnitude greater within thefinancial system (Mishkin et al. 2011)
And almost two orders of magnitude greater for the global
economy (Luttrell et al. 2013)

Additional requirement: account for amplification ofclimate-related shocks through financial interlinkages(Battiston et al. 2012, Elliott et al. 2014, Acemoglu et al.
2015).

Introduction

Physical risks

Transition Risks

Notions of risk
In (mathematical) finance, risk is mainly understood as the variability of
returns as measured by variance, volatility, risk-measures...
The assessment and the management of risk is somehow"consequentialist": rather than on its drivers, one focus on its
materialisation in market prices.
Furthermore, it is generally assumed that all relevant information aboutrisk is reflected in asset-prices.
The broader (disaster) risk literature adopts a more holistic perspective
on risk:
RISK = EXPOSURE × HAZARD × VULNERABILITY
whereby:Exposure/Exposition: assets that are potentially affected
and their characteristics (e.g. buildings in flood plain)
Hazard/Alea: Nature and distribution of potential
impact/shocks (e.g. distribution of potential flood level)
Vulnerability: Scale/Value of damages given hazard (e.g.
depth damage function) .

Introduction

Physical risks

Transition Risks

Climate related risks
Physical Risk
Exposure: assets (directly or indirectly) exposed toclimate-related natural hazards.
Hazard: changes in the distribution of natural hazards
induced by climate change.
Vulnerability: Impact of natural hazards on the performanceof economic actors and/or the value of financial assets

Transition Risk
Exposure: assets in sectors (directly or indirectly) impacted
by climate policy.
Hazard: Timing and Intensity of climate policy
Vulnerability: Impact of climate policy on the performanceof economic actors and/or the value of financial assets.

Introduction

Physical risks

Transition Risks

Risk in climate economics
Risk, in the sense of variability, is mostly absent from climate economic
models, which focus on the trade-off between mitigation costs andexpected future damages.
e.g. DICE model (Nordhaus 1992...) :P
[Ct/Lt ]1−α max(Ct ,µt )t=1,··· ,T Wt = Tt=1 (1 + ρ)−t Lt

1−α




s.t




Qt = It + Ct
Kt = It + (1 − δ)Kt−1

Qt = Ωt (1 − θµτt )At Ktγ L1−γt
1 + ΩtΩt = ψ1 Tt + ψ2 Tt2(“judgmental" calibration)E(t) = σ(t)(1 − µ(t))At Ktγ L1−γtTt = f ((Eν )ν≤t )(climate module)
where Wt ; social welfare, Lt : population, Ct : consumption, It :investment, Qt : production, Kt : capital, µt : mitigation/abatment effort,Ωt : climate impact, Tt : temperature, σ(t) : carbon intensity, Et :emissions.

Introduction

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Transition Risks

Risk in climate economics
Absence of risk-perspective in climate economic models
harshly criticised (Pyndick 2013)“A plethora of integrated assessment models (IAMs) have been
constructed and used to estimate the social cost of carbon(SCC) and evaluate alternative abatement policies. Thesemodels have crucial flaws that make them close to useless as
tools for policy analysis: certain inputs (e.g. the discount rate)
are arbitrary, but have huge effects on the SCC estimates themodels produce; the models’ descriptions of the impact ofclimate change are completely ad hoc, with no theoretical or
empirical foundation; and the models can tell us nothing
about the most important driver of the SCC, the possibilityof a catastrophic climate outcome."

Introduction

Physical risks

Transition Risks

Risk and uncertainty in the IPCC framework.
Account for uncertainties associated to socio-economic developments
through scenario-analysis (no probability).
Scenarios constructed by the combination of a representative
concentration pathway (RCP) and a socio-economic pathway (SSP).

A standard for interactions between (i) climate/earth system modeling ,(ii) assessment of impacts, adaptation and vulnerability, (iii) integrated
assessment modeling (understanding the drivers of climate change andthe effectiveness of potential mitigation policies).

Introduction

Physical risks

Outline1

Introduction

2

Physical risks

3

Transition Risks

Transition Risks

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Transition Risks

Agenda1

Hazard: changes in the distribution of natural hazards
induced by climate change.

2

Exposure: assets (directly or indirectly) exposed toclimate-related natural hazards.

3

Vulnerability: Impact of natural hazards on the performanceof economic actors and/or the value of financial assets.
Example of coastal floods: (DIVA model, Hinkel et al.
2014)

Introduction

Physical risks

Transition Risks

Hazard: changes in the distribution of extreme
sea-levels
Existing data on global observations of extreme sea levels: global
extreme sea-level analysis: www.gesla.org.
Projections on mean sea-level rise (for each RCP) use results fromclimate models (GCMs) to estimate oceanic thermal expansion (stericeffect), mass changes from glaciers as well as the Greenland andAntarctic ice sheets (each require specific model).
Beyond RCPs, three scenarios for ice contribution (accounting for
uncertainties on ice dynamics )Introduction

Physical risks

Transition Risks

Hazard: changes in the distribution of extreme
sea-levels
For each RCP, projected distribution of extreme sea-levels obtained bytranslating historical distribution by mean-sea level.
Let fx (y ) be the probability density for an event (yearly maximum) of
elevation y ∈ R+ at location (coastal segment) x ∈ X
For each rcp and ice scenario, one has a mean sea-level rise y (rcp,ice) ,and the resulting hazard density:(rcp,ice)fx(x, y ) = fx (y − y (rcp, ice)).

Coasts are protected (by dikes), the relevant hazard are these that
overtop the dykes (and then everything goes as if the dyke breaks).
If h(x) is the height of the dyke at location x the law of the (yearly(rcp,ice,h)maximum) flood level Lx
at location x is given by!
Z h(x)
Z l(rcp,ice,h)(rcp,ice)(rcp,ice)P(Lx≤ l) :=
fx(z)dz 1[0,+∞[ (l) +
fx
(z)dz
0

h(x)

Introduction

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Transition Risks

Direct exposure: assets exposed to coastal floods
12000 coastal segments considered.
Area exposed to flooding obtained from digital elevation model, yields
ax (l) : area under elevation l at location x(Historical) population exposed obtained from population density
dataset (GRUMP or LANDSCAN), yields px (l) : population leaving
under elevation l at location x.
Asset exposed determined by applying subnational GDP per capita
rates to the population data and an assets-to-GDP ratio of 5 2 , yields
kx (l) : value of (physical) assets under elevation l at location x.

Future exposure is obtained by applying national population and GDP
growth rates of SSPs to the coastal segments, yields scenario-specificpopulation, pxssp (l), and assets , kxssp (l), exposure.
2“ which is the equilibrium value found in a standard growth model with a labor elasticity of production of 0.2"
(Hinkel et aL. PNAS (2014).

Introduction

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Transition Risks

Vulnerability
Vulnerability of assets measured through logistic
depth-damage functions: given inundation level l, a sharev (l) of the value of assets is destroyed.
Overall, given scenarios rcp, ice, ssp and protection levelh, the risk on physical assets at location x is given by
v (l)
|{z}

(rcp,ice,h)k ssp (l) d L| x {z } | x {zvulnerability exposure

hazard(l)}Most of the analysis in climate economics focus on
expected costs/risk (assuming events occur independently
over locations):
Z Z(rcp,ice,h)ssp
v (l)kx (l)d Lx
(l)
X

L

Introduction

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Transition Risks

Adaptation

A key driver of risk is the level of protection h.
Current levels of protection given by empiricalobservations.
Dynamics of h are given by adaptation scenario:
No adaptation scenario: constant dyke height.
Adaptation scenario: dyke raised with sea-level (sameprobability that dyke is overtopped but larger flooding
levels).
Optimal adaptation scenario: dykes raised to optimal level
determined by cost-benefit analysis.

Introduction

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Transition Risks

1

1

0.8

0.8

0.6

0.6

F(x)

F(x)

Distribution of direct economic impacts from coastal
floods, constant protection standards

0.4

0.4

0.2

0.2

0

10-2

10-1

100

x: Share of World GDP

101

0

10-2

10-1

100

101

x: Share of World GDP

Figure: Simulated cumulative distribution of impacts with (left) and without(right) adaptation for SSP5 and RCP 8.5 at time horizons 2030(blue), 2050
(red) and 2080 (yellow).
Increase in risk over time in terms of first-order stochastic dominance,
i.e. for every risk level r :P(R 2080 ≥ r ) ≥ P(R 2050 ≥ r ) ≥ P(R 2030 ≥ r ) ≥ P(R hist ≥ r )Introduction

Physical risks

Transition Risks

Distribution of risks from coastal floods

Figure: Histogram of the distribution of impacts with (left) and without(right) adaptation for SSP5 and RCP 8.5 at time horizons 2030(blue),
2050 (yellow) and 2080 (red). Historical distribution in green.

Introduction

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Transition Risks

Summary of required models and datasets.
Global circulation models from CMIP5 (Coupled Model
Intercomparison Project).
Global Extreme Sea Level Analysis dataset.
Glacier (Randolph Glacier Inventory)
Antartica Ice modelGreenland Ice model
Population density datasets: Global Rural-Urban Mapping
Project (GRUMP), LandScan High Resolution global
Population Data Set.
Population and GDP projections from socio-economic
scenarios.

Introduction

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Transition Risks

Financial stability and indirect exposure

Concern about financial stability.
Barrot and Sauvagnat (2016) find econometric evidence of
the impact of natural catastrophes on sales and market
value of firms and their main customers (impact depend on
input specificity).
Klomp (2014) find econometric evidence that natural
catastrophes decrease distance of commercial banks todefault.

Introduction

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Transition Risks

Shock propagation in financial networks I
Quantitative models of shock propagation in financial networks(Battiston et al. 2012... ).
i = 1, · · · , N financial actors characterized by:“external" assets Ai ∈ R+ .
Assets/liabilities towards financial actors (Bi,j )i,j=1,···N where
Bi,j (resp.Li,j ) represents assets (resp. liabilities) of i
towards j.
P
PEquity of actor i given by Ei = Ai + j Bi,j − k Li,kBankruptcy risk:

Actor i faces (common knowledge/expected) idiosyncratic
shock i , uniformly distributed over [0, Ki ] with Ki > Ei .
If i > Ei , actor i goes bankrupt and defaults on its liabilities(zero recovery in the short run).
Financial liabilities valued in a risk-neutral manner
Bi,j = P(j < Ej )Lj,i = [Ej/Kj ]Lj,iwhere Lj,i is nominal value of liabilities.

Introduction

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Transition Risks

Shock propagation in financial networks II
An unexpected (e.g. climate-induced) shock µj ∈ RN+
affects actors j.
The equity of actor j is reduced to Ej (1) = Ej − µi .
The default probability of actor j increases accordingly andthe book value of its liabilities decrease toBi,j (1) := [Ej (1)/Ej ]Bi,j .
The shocks propagate in the network over time:PP
Ei (t + 1) = max(0, Ei (t) + j Bi,j (t) − k Lk ,i )Bi,j (t + 1) = [Ej (t+1)/Ej (t)]Bi,j (t).

A number of variants for stopping conditions and/or
conditions under which an actor is “contagious" (Battiston
et al. 2012, Bardoscia 2015, Barucca et al. 2016)

Introduction

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Transition Risks

Impacts of climate shocks on financial assets: data
As if analysis 3 : impacts of future (relative) damage
distribution given current assets and exposures.
Analysis at the country/institutional sector level: private
sector, government sector, insurance, financial (excl.
insurance).
Data on the value of “equity": private sector (physicalcapital - private debt as % of gdp, from WB/IMF), banking
sector (bank capital as % of gdp from IMF).
Data on exposure: reconstruct domestic and international
exposure of the financial sector using data on domestic
public/private debt (IMF) and international bilateralexposures through equity, debt and interbank (EUconsolidated dataset from IMF and BIS).
3

Ciscar et al. (2011) "Physical and economic consequences of climatechange in Europe." PNAS 108.7 (2011): 2678-2683

Introduction

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Transition Risks

Impacts of climate shocks on financial assets:
domestic allocation of shocks

Si =

αi Si
|{z}

Ii : insured share+ min(φi , max(0, σ − αi )Si ) + 1 − Ii − Gi|{z
} | {z }Gi : gov share

Pi : priv share

where:
Si total shock in country i.
αi , share of insured damages (Munich Re data)σ parameter giving maximal total coverage (insurance role of the state)
φi is fiscal space.
Private sector shock is residual.

Introduction

Physical risks

Transition Risks

Domestic propagation of shocks
Shocks transmitted to the domestic banking sector:
First-order approximation of impact on liabilities fromprivate sector:
value of liabilities proportional to equity Ei
impact of a shock Pi proportional to (Ei −Pi )/Ei

First-order approximation of impact on liabilities fromgovernment sector:
value of liabilities proportional to difference between current
debt Di and maximal sustainable debt D i .
impact of a shock Gi proportional to (Di −Di −Gi )/Di −Di

Share γIi/Bi of insured damages (where Bi is insurance
capital buffer, 99.5 percentile following Solvency II)

Introduction

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Transition Risks

Domestic allocation of shocks

Share of World GDP

2

1.5

1

0.5

0
10 0

10 1

10 2

Figure: Components of the domestic impact of floods for a sample ofevents from the high-impact scenario with adaptation at horizon 2080.
The total height of the graph corresponds to the total domestic
damages. It is allocated between the insurance sector (blue), thegovernmental sector (red), private sector (yellow), and domestic
impacts on the financial sector (purple).

Introduction

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Transition Risks

International propagation of shocks

Impacts from international holding of equity and debt.
Propagation within the financial sector according to
debtrank algorithm:
P
PN
Fi (t + 1) = Fi (t) + {j |Fj (t)>0} Bi,j (t) − j=1 Li,jFj (t)B (t)Bi,j (t + 1) =Fj (t − 1) i,j

where Fi is equity of banking sector in country i, Bi,j (resp. Li,j )assets (resp. liability) of banking sector of country i relative tobanking sector of country j.

Introduction

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Transition Risks

Global impacts on financial stability
Amplification of shocks on country i depends on leverage and network
centrality of banking sector.
Total impact depends on magnitude of domestic impact and financial
network characteristics
250

Eigenvector centrality

UK

200
US

150
DE

100

FR

50
0

CN

JP
CA
IT
AU ES

0

20

NL
CH

SG

40

60

80

100

120

Financial Leverage

Figure: financial leverage (assets outstanding of domestic financial
sector on foreign financial sectors in percentage of gdp) vs eigenvector
centrality in bilateral financial exposure network

Introduction

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Transition Risks

Single-country var event
Country
CN
UK
DE
US
FR
CA
IT
DK
NO
IN

historicalglobal
16.5
8.6
5.1
2.8
2.5
1.2
1.1
1.1
0.7
0.7

direct.
15.2
2
2.8
1.9
0.8
0.5
0.4
0.3
0.3
0.7

adaptation
Country global direct
CN
21.5
19.7
UK
10.5
2.5
US
6.5
4.3
DE
5.7
3.1
FR
3
1
IT
2.8
1.1
NO
2.6
0.8
EG
2.5
1.5
IN
2.3
2.1
CA
2.2
0.9

no adaptation
Country global direct
UK
244.7
27
CN
196.2 165.9
JP
130.5
48.9
US
86.3
31.8
DE
61.8
20.5
FR
61.1
9
CA
37.8
7.7
SG
33.2
3.7
IT
27.8
9.4
DK
27.3
4.8

Table: Impact induced by a 95th percentile coastal flood event (yearlydamages) occurring in a single country. Results refer to the ten mostimpactful countries and the high-impact scenario at the horizon 2080
with and without adaptation. The direct impact corresponds to thevalue of direct damages induced by the event in the origin country,
the global impact corresponds to the sum of direct and indirect losses
in capital in the financial and private sectors of all countries.

Introduction

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Transition Risks

Single country var-event

Sizeable increase, among an approx. fixed pool ofcountries, in the scenario with constant protection
standard.
Order of magnitude increase in the scenario with historicalprotection: developed countries are hit and amplify
massively the shocks because of financial leverage.

Introduction

Physical risks

Compound event

Independent realizations of shocks in each country.
Evolution of the total distribution of damages.

Transition Risks

Physical risks

Transition Risks

1

1

0.8

0.8

0.6

0.6

F(x)

F(x)

Introduction

0.4

0.4

0.2

0.2

0

10-2

10-1

100

x: Share of World GDP

101

0

10-2

100

x: Share of World GDP

Figure: Empirical cumulative distribution of the global financial impact,
including financial propagation, induced by coastal floods with (left) andwithout (right) adaptation for the high-impact scenario. Each panel displays
the historical distribution of damages (purple), and the distribution under thehigh-impact scenario at horizons 2030 (blue), 2050 (red) and 2080 (yellow).
Damages are measured in percentage of world gdp and empirical distribution
is obtained from 50000 independent monte-carlo simulations in which
damage realizations in each country are drawn independently.

Introduction

Physical risks

Total impact with adaptation

Transition Risks

Introduction

Physical risks

Total impact without adaptation

Transition Risks

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Transition Risks

Amplification of risk
Ratio of risk-indicator with respect to historical values forselected impacts under SSP5-2080 scenario.

Increase in risk consistently observed across measures
Largest impact on the mid-part of the right tail (60th-70th
percentiles).

Introduction

Physical risks

Sensitivity Analysis

Main parameters of the model:
insurance buffer,rate of transmission insurance-finance (γ),
total share covered (σ),
fiscal space,max. sustainable debt.

Major effects of σ and γ.

Transition Risks

Introduction

Physical risksσ
SLR var-80
ρ
SLR var-80
D ( % of GDP)SLR var-80

0.25
7.52440.25
5.3884200
7.5244

0.5
6.66390.5
5.8312300
7.4989Transition Risks

0.75
6.22050.75
6.2586400
7.49081
5.2462
1
6.6639500
7.4862

Table: Impact on the value-at-risk at the 80th percentile (measured asshare of world gdp) in the high-impact scenario without adaptation ofvariations in the value of the share σ of the shock covered (by the
insurance and the governmental sector), the rate ρ of transmission ofshocks from the insurance sector to the financial sector and of the
maximal sustainable level of debt D. Non-varying parameters are set
to their default values.

Introduction

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Transition Risks

Summary: distribution of impacts

Larger impact on the tail of the distribution than on themean.
Adaptation is key:
With adaptation: 20% to 50 % increase in risk-level.
Without adaptation: order of magnitude increase in risk.

Introduction

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Transition Risks

Summary: Allocation and propagation of impacts

From the point of view of financial risk, the more buffers
between the financial system and climate shocks there are,the better.
From the domestic perspective:
Limit contagion from/Increase resilience of the insurance
sector.
Government can act as a buffer if contagion less acutethrough government bonds.

From the global perspective:
Require higher adaptation standards in countries withhigher financial leverage.

Introduction

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Transition Risks

Summary: Policy implications

Required investments in adaptation.
Capital requirements for the insurance sector (anticipating
impacts of climate change).
Insurance role of the government.
Future climate impacts accounted for in investment
decisions (capital requirements again)

Introduction

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Transition Risks

References
Greenlaw D, Hatzius J, Kashyap AK, Shin HS (2008) Leveraged losses: lessons from the mortgage market
meltdown.
Mishkin FS (2011) Over the cliff: From the subprime to the global financial crisis. Journal of 510 Economic
Perspectives 25(1):49–70. 511 5.
Luttrell D, Atkinson T, Rosenblum H (2013) Assessing the costs and consequences of the 2007–09 financial
crisis and its aftermath. Economic Letter 8.
Battiston, S., Puliga, M., Kaushik, R., Tasca, P., Caldarelli, G. (2012). Debtrank: Too central to fail? financial
networks, the fed and systemic risk. Scientific reports, 2, 541
Acemoglu, D., Ozdaglar, A., Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks.
American Economic Review, 105(2), 564-608.
Elliott, M., Golub, B., Jackson, M. O. (2014). Financial networks and contagion. American Economic
Review, 104(10), 3115-53.
Nordhaus, W. D. (2014). A question of balance: Weighing the options on global warming policies. Yale
University Press.
Nordhaus, W. D. (1992). An optimal transition path for controlling greenhouse gases. Science, 258(5086),
1315-1319.
Pindyck, R. S. (2013). Climate change policy: What do the models tell us?. Journal of Economic Literature,
51(3), 860-72.
Hinkel, Jochen, et al. "Coastal flood damage and adaptation costs under 21st century sea-level rise."
Proceedings of the National Academy of Sciences 111.9 (2014): 3292-3297.
Barrot, J. N., and Sauvagnat, J. (2016). Input specificity and the propagation of idiosyncratic shocks in
production networks. The Quarterly Journal of Economics, 131(3), 1543-1592.
Klomp, J. (2014). Financial fragility and natural disasters: An empirical analysis. Journal of Financial
Stability, 13, 180-192.
Battiston, Stefano, et al. "Debtrank: Too central to fail? financial networks, the fed and systemic risk."
Scientific reports 2 (2012): 541.
Bardoscia, M., Battiston, S., Caccioli, F., and Caldarelli, G. (2015). DebtRank: A microscopic foundation forshock propagation. PloS one, 10(6).
Barucca, P., Bardoscia, M., Caccioli, F., D’Errico, M., Visentin, G., Battiston, S., and Caldarelli, G. (2016).
Network valuation in financial systems. Available at SSRN 2795583.

Introduction

Intermission

A la semaine prochaine !

Physical risks

Transition Risks

Introduction

Physical risks

Outline1

Introduction

2

Physical risks

3

Transition Risks

Transition Risks

Introduction

Physical risks

Transition Risks

Components of transition risk

RISK = EXPOSURE × HAZARD × VULNERABILITY

1

Exposure: economic and financial assets in sectors
directly or indirectly impacted by climate-policy.

2

Hazard: timing and intensity of climate policy

3

Vulnerability: impacts of climate policy shocks on asset
value.

Introduction

Physical risks

Transition Risks

Climate policy: general equilibrium analysis

General equilibrium model with L goods, n producers withproduction set Yj ∈ RL , m consumers with utility
ui : RL+ → R+ , initial endowment ei ∈ RL+ , shares θi,j ∈ R+ .
Equilibrium defined as price p∗ ∈ RL+ , consumption plans(xi∗ )i=1,···m ∈ (RL+ )m , production plans (yj∗ )i=1,···n ∈ (RL+ )nsuch that:∀j = 1, · · · n, yj∗ = argmaxyj ∈Yj p∗ · yj
∀i = 1, · · · m, xi∗ = argmax{xi ∈RL+ |p·xi ≤p·ei +Pj θi,j p∗ ·y ∗ ui (xi )jPm ∗ PnPm∗
i=1 xi =j=1 yj +i=1 ei

Production yj ∈ Yj induces ghg emissions fj (yj ).

Introduction

Physical risks

Transition Risks

Climate policy: genenal equilibrium analysis
Emissions unconstrained initially but climate policy
induces, via market or tax, a cost q on emissions.
New equilibrium given by p, (x̄i ), (ȳj ) such that:∀j = 1, · · · n, ȳj = argmaxyj ∈Yj p · yj − qfj (yj )∀i = 1, · · · m, x̄i = argmax{xi ∈RL+ |p·xi ≤p·ei +Pj θi,j pȳj −qfj (ȳj ) ui (xi )PmPnPm
i=1 x̄i =j=1 ȳj +i=1 eiN.B: initial equilibrium such that∀j = 1, · · · n, yj∗ = argmaxyj ∈Yj p∗ · yj
∀i = 1, · · · m, xi∗ = argmax{xi ∈RL+ |p·xi ≤p·ei +Pj θi,j p∗ ·y ∗ ui (xi )jPm ∗ PnPm∗
i=1 xi =j=1 yj +i=1 ei

General equilibrium effects: each actor potentially affectedby carbon price.

Introduction

Physical risks

Transition Risks

Integrated assessment models

Family of general-equilibirum integrated-assessment
models (see https://www.iamcdocumentation.eu).
Basics model à la Nordhausmax(C ,µ )t t t=1,··· ,T



s.t






















Wt =

PT

t=1 (1 + ρ)

−t

1−α

Lt

[Ct/Lt ]1−αQ t = I t + Ct
Kt = It + (1 − δ)Kt−1
Ωt
γ 1−γ
Qt =
(1 − θµτt )At Kt Lt
1 + ΩtΩt = ψ1 Tt + ψ2 Tt2(“judgmental" calibration)
γ 1−γE(t) = σ(t)(1 − µ(t))At Kt Lt
Tt = f ((Eν )ν≤t )(climate module)

Introduction

Physical risks

Transition Risks

Integrated assessment models+ exogenous constraints on emissions or temperature(climate policy).
+ detailed representation of energy input in production:
max(C ,µ )t t t=1,··· ,T



s.t








































Wt =

PT

t=1 (1 + ρ)

−t

1−α

Lt

[Ct/Lt ]1−αQt = It + Ct
Kt = It + (1 − δ)Kt−1ΩtΦ(Kt , Lt , Ut )Qt =1 + ΩtUt = χ(Utr , Utf )PTff
t=1 Ut ≤ Ur
Utr ≤ αrt (·)V tΩt = ψ1 Tt + ψ2 Tt2E(t) = ζ(Utf )Tt = f ((Eν )ν≤t )maxt Tt ≤ T+ spatial structure (weighted average of regional utilites).

Introduction

Physical risks

Transition Risks

Representation of the production process in iams.

Figure: Schematic representation of the production process in the Witch
model (source: www.iamcdocumentation.eu)

Introduction

Physical risks

Transition Risks

Usage of IAMs for climate policy assessment

Variety of assumptions/models about technologicalstructure and technological progress.
Assess economic impacts of different climate policy
scenarios: climate objective but also its international
implementation.
Relevant output: carbon price, market share of different(energy) subsectors in each country/region (link to CPRS)Introduction

Physical risks

Transition Risks

Exposure: empirical perspective

Structured micro-economic data on firms mostly available
in the form of sectoral classification (NACE, NAICS, ISIC),
not production function.
Structure of the classification (aggregate categories) not
appropriate to characterise exposure to climate policy, e.g.
hard to distinguish energy sources or inputs to the fossil
fuel industry.

Introduction

Physical risks

Exposure: empirical perspective

Climate-policy relevant sectors identified based on their GHG
emissions, their role in the energy supply chain, and existence ofrelated climate policy institutions.
Remap standard classification of economic activities (NACE) into
climate-policy relevant sectors (CPRS 1 and CPRS 2)

Transition Risks

Introduction

Physical risks

Transition Risks

Exposure: portfolio analysis

Figure: Breakdown of exposures by institutional sector and CPRS (level 1)
through bonds. Source Battiston et al. for EU technical expert group on
sustainable finance.

Introduction

Physical risks

Transition Risks

Exposure: portfolio analysis

Figure: Breakdown of outstanding bond amount (left) and market
capitalisation (right) from EU NFCs by CPRS (level 1 and 2) overtime. Source Battiston et al. for EU technical expert group on
sustainable finance.

Introduction

Physical risks

Transition Risks

Exposure: portfolio analysis

Figure: Breakdown of exposures in the syndicated loan portfolio of U.Sbanks and/or U.S. subsidiaries of international banks (dealscan data, Mandel
et al. forthcoming).

Introduction

Physical risks

Hazards: uncertainty about climate policy

Figure: RCPs and associated uncertainties range for emission
pathways

Transition Risks

Introduction

Physical risks

Transition Risks

Hazard: economic assessment of climate policy

Family of general-equilibirum integrated-assessment
models (see https://www.iamcdocumentation.eu).
Basics model à la Nordhausmax(C ,µ )t t t=1,··· ,T



s.t






















Wt =

PT

t=1 (1 + ρ)

−t

1−α

Lt

[Ct/Lt ]1−αQ t = I t + Ct
Kt = It + (1 − δ)Kt−1
Ωt
γ 1−γ
Qt =
(1 − θµτt )At Kt Lt
1 + ΩtΩt = ψ1 Tt + ψ2 Tt2(“judgmental" calibration)
γ 1−γE(t) = σ(t)(1 − µ(t))At Kt Lt
Tt = f ((Eν )ν≤t )(climate module)

Introduction

Physical risks

Transition Risks

Hazard: economic assessment of climate policy+ exogenous constraints on emissions or temperature(climate policy).
+ detailed representation of energy input in production:
max(C ,µ )t t t=1,··· ,T



s.t








































Wt =

PT

t=1 (1 + ρ)

−t

1−α

Lt

[Ct/Lt ]1−αQt = It + Ct
Kt = It + (1 − δ)Kt−1ΩtΦ(Kt , Lt , Ut )Qt =1 + ΩtUt = χ(Utr , Utf )PTff
t=1 Ut ≤ Ur
Utr ≤ αrt (·)V tΩt = ψ1 Tt + ψ2 Tt2E(t) = ζ(Utf )Tt = f ((Eν )ν≤t )maxt Tt ≤ T+ spatial structure (weighted average of regional utilites).

Introduction

Physical risks

Transition Risks

Representation of the production process in iams.

Figure: Schematic representation of the production process in the Witch
model (source: www.iamcdocumentation.eu)

Introduction

Physical risks

Transition Risks

Usage of IAMs for climate policy assessment

Variety of assumptions/models about technologicalstructure and technological progress.
Assess economic impacts of different climate policy
scenarios: climate objective but also its international
implementation.
Relevant output: carbon price, market share of different(energy) subsectors in each country/region (link to CPRS)Introduction

Physical risks

Transition Risks

Hazards: uncertainty about sectoral and geographicalimpacts

Figure: Market share of coal in Sub-Saharan Africa (left) and of
renewables in Latin America (right) in secondary energy production
for bau, 500ppm et 450ppm scenarios

Introduction

Physical risks

Transition Risks

Hazards quantified

For each technology/subsector (e.g. Electricity|Fossil) k ina climate-policy relevant sector, each climate-policy
scenario c and each date τ , iam models yield a shockf (k , c, τ ) on the market share of the technology in case ofa policy shift/disorderly transition to scenario c at date τ.
Transition risk: impact on asset value ?
Working assumption:"The NGFS recognises that there is astrong risk that climate related financial risks are not fullyreflected in asset valuation."
Problematic: how to integrate economic information in
asset valuation ?

Introduction

Physical risks

Transition Risks

Vulnerability of asset values.
Assuming a firm’s asset values (i.e. asset side of thebalance sheet) are proportional to market share, shock on
corporate asset value determined by technologicalcomposition of its revenues, i.e. for t ≥ τ :At (c, τ ) = (1−s(c, τ ))Ãt :=X

wk (1−f (k , c, τ ))Ãt +(1−k ∈CPRSX

wk )Ãtk ∈CPRS

where Ãt value of assets if market ignores climate shocks.

N.B. Linear impact on asset value can be seen either as
first-order approximation or “rationalized" by assuming (i)total market size not affected by the shock, (ii) value
proportional to market share.
N.B.2: this assumes that the market will not price the risk
until it materializes.
N.B.3: Rather than a deterministic shock, one can infer
distribution of shocks from family of models.

Introduction

Physical risks

Transition Risks

Vulnerability of asset values.

Given impact on firm’s asset value, one can use structural
credit risk model to assess impact on the value of its
financial liabilities.
e.g. Merton model assumes d Ã = Ã(µÃ dt + σÃ dWt ) anddebt structure captured by zero-coupon debt of nominal L
and maturity T . Then:
Debt valued as safe-claim minus European put
DT = L − max(L − ÃT , 0).
Equity values a European call ET = max(L − ÃT , 0).

Introduction

Physical risks

Transition Risks

Vulnerability of asset values.
In absence of climate-shocks, debt-value given by Blackand Scholes formula.
2σ√ ± A/2)
Dt = L exp(−r (T −t))N(d− )+ Ãt N(d+ ) where d± = log(Ãtσ/L)+(rT −tÃFollowing climate-shock (c, τ ), assets are reevaluated
If t < τ :If t ≥ τ :Dt (c, τ ) = Dt
Dt (c, τ ) = L exp(−r (T − t))N(d− ) + At (c, τ )N(d+ )where d± =2log(At (c,τ )/L)+(r ±σ /2)
Ã
√
σ
T −t
Ã

The latter the transition occurs the larger the gap to bridge
to be aligned with the 2◦ C scenario and thus the larger theshock.

Introduction

Physical risks

Transition Risks

Micro-economic assumptions underlying thevulnerability model
He and Leland (1993): setting with one unit risky asset and one risklessbond in zero-net supply (assume zero interest)
Representative agent initially holds risky asset, chooses holding of risky
asset through time I(t), and consumes at final date T only.
Diffusion process d Ã = Ã(µÃ dt + σÃ dWt ) is equilibrium asset-price if
holding only risky asset is solution to
E(U(X (T ))) supIs.t
dX (t) = µÃ I(t)dt + σÃ I(t)dWt

X (t) ≥ 0
Diffusion consistent with equilibrium if U exhibits constant-relative risk
00
aversion and XU (X )/U 0 (X ) = µÃ/σÃ2 .
In particular, similar diffusion model can be consistent before and aftershockIntroduction

Physical risks

Transition Risks

Treatment of ambiguity
Even if scenario (e.g. 2◦ C scenario) is fixed, uncertainty
about timing non-probabilistic.
Decision under uncertainty:
α- maxmin approach (Arrow and Hurwicz 1972, Gilboa andSchmeidler 1989, Jaffray and Philippe 1997, Ghirardato etal. 2004):
D t (α) = α min Dt (c, τ ) + (1 − α) max Dt (c, τ )(c,τ )(c,τ )

Smooth-amiguity approach à la Klibanoff et al. 2005:
ZD̃t =φ(DT (c, τ )) dπ(c, τ )(c,τ )

Introduction

Physical risks

Transition Risks

Impact of uncertainty on asset valuation

Figure: Impact of climate shocks on the value of a zero-coupon of face-value
60 for a composite emitter built proportionally to the composition of eurostoxx
60 oil and Gas for increasing maturity and varying levels of ambiguity
aversion in the context of a transition to scenario LIMITS 450 (average shockover IAMs) .

Introduction

Physical risks

Transition Risks

Risk-equivalent impact of Climate Shocks

Figure: Impact of climate shocks on the risk-neutral probability of default of azero-coupon of face-value 60 for a composite emitter built proportionally tothe composition of eurostoxx 60 oil and Gas for increasing maturity andvarying levels of ambiguity aversion. Gap widens with maturity as potential
magnitude of climate shock increases

Introduction

Physical risks

Transition Risks

Impact of timing of Climate Shocks

Figure: Value of debt (left) and risk-neutral default probability (right) whenshock can’t occur before period 10.

Introduction

Physical risks

Transition Risks

Impact of timing of Climate Shocks

Figure: percentage Impact on the value of 5 years zero-coupon of atransition to scenario LIMITS 450 (average shock over IAMs) for an ambiguity
averse (α = 0.1) and an ambiguity « neutral » (α = 0.5) decision-maker.
Sample of Stocks from EUROSTOXX Oil and Gas and Utilities (illustrativevalues).

Introduction

Physical risks

Transition Risks

Second-round effects: propagation of shocks
BalanceP
sheet of financial institutiont:
PN f i at datef (t) −
e (t) − Le where F
Fi (t) = {j |Fj (t)>0} Bi,jL+Bi
j=1 i,jiib (resp. Lb ) interbank assets (resp.
is equity of bank i, Bi,ji,jliability) and Bie (resp. Lei ) interbank asset (resp. liability).
Following a shock on external assets Bie (1) < Bie (0).
Propagation of shocks within the financial sector according
to debtrank algorithm.
For t ≥ 2 :PP

Fi (t+1) = Fi (t)+ {j |Fj (t)>0} Bi,j (t)−Fj (t)
Bi,j (t + 1) =B (t)Fj (t − 1) i,j{j |Fj (t−1)>0} Bi,j (t−1)

Given common shock s on value of external assets,
girst-order approximation of shocks on bank i :h(i) := `ei + `bi `ewhere `ei = Bie/Fi and `bi =P

j Bi,j/Fi .

Introduction

Physical risks

Transition Risks

Structure of leverage

Figure: Vulnerability (to external shocks) vs Impact (on the system incase of default) in 2008 and 2013. Circle size reflects asset size,
colors reflect the magnitude of the interbank leverage. The four
quadrants divide the banks into four categories

Introduction

Physical risks

Transition Risks

Second-round effects

Figure: First and Second round losses for 100% fossil+utilities shock.
Source Battiston et al. 2017

Introduction

Physical risks

Transition Risks

Summary
Concerns that the impacts of climate policy/transition risks
are not reflected in asset valuation.
Integrated assessment models can be used as a source of
information on climate policy shocks.
Economic assessment of climate policy shocks can beused as input to structural credit risk models undersimplifying assumptions.
Ambiguity/uncertainty about the timing and the stringencyof climate policy can be captured throughdecision-theoretic models.
Mostly empirical work, missing analytical framework
bridging production theory to asset valuation

Introduction

Physical risks

Transition Risks

References

Arrow, K. J., and Hurwicz, L. (1972). An optimality criterion for decision-making under ignorance.
Uncertainty and expectations in economics, 1.
Battiston, S., Mandel, A., Monasterolo, I., Schütze, F., and Visentin, G. (2017). A climate stress-test of thefinancial system. Nature Climate Change, 7(4), 283-288.
Gilboa, I., and Schmeidler, D. (1989). Maxmin expected utility with non-unique prior. Journal of
Mathematical Economics, 18(2), 141-153.
Jaffray, J. Y., and Philippe, F. (1997). On the existence of subjective upper and lower probabilities.
Mathematics of Operations Research, 22(1), 165-185.
Ghirardato, P., Maccheroni, F., and Marinacci, M. (2004). Differentiating ambiguity and ambiguity attitude.
Journal of Economic Theory, 118(2), 133-173.
Klibanoff, P., Marinacci, M., and Mukerji, S. (2005). A smooth model of decision making under ambiguity.
Econometrica, 73(6), 1849-1892.

Introduction

Physical risks

Transition Risks

Complements on ambiguity
If Q denotes the risk-neutral probability inferred frommarket-data.
α- maxmin approach:D t (α) = α min e−r (T −t) EQ [min(L, (1−s(c, τ ))AT ]+(1−α) max e−r ((c,τ )(c,τ )

Smooth-amiguity approach
Z
D̃t =



φ e

−r (T −t)EQ [min(L, (1 − s(c, τ ))AT ] dπ(c, τ )